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Can Social Features Help Learning to Rank YouTube Videos?

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Web Information Systems Engineering - WISE 2012 (WISE 2012)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 7651))

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Abstract

We investigate the impact of social features (such as likes, dislikes, comments, etc.) on the effectiveness of video retrieval in YouTube video sharing system using state-of-the-art learning to rank approaches and a greedy feature selection algorithm. Our experiments based on a dataset of 3,500 annotated query-video pairs reveal that social features are promising to improve the retrieval performance.

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Author information

Authors and Affiliations

  1. L3S Research Center, Hannover, Germany

    Sergiu Viorel Chelaru, Claudia Orellana-Rodriguez & Ismail Sengor Altingovde

Authors
  1. Sergiu Viorel Chelaru
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  2. Claudia Orellana-Rodriguez
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  3. Ismail Sengor Altingovde
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Editor information

Editors and Affiliations

  1. School of Computer Science, Fudan University, 825 Zhangheng Rd., Shanghai, 201203, China

    X. Sean Wang

  2. Department of Computer Science, College of Engineering, Science and Engineering Offices, The University of Illinois at Chicago, 851 South Morgan Street (M/C 152), 60607-7053, Chicago, Illinois, USA

    Isabel Cruz

  3. Department of Informatics and Telecommunications, University of Athens, GR15784, Ilisia, Athens, Greece

    Alex Delis

  4. Centre for Applied Informatics, Victoria University, PO Box 14428, 8001, Melbourne, VIC, Australia

    Guangyan Huang

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© 2012 Springer-Verlag Berlin Heidelberg

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Chelaru, S.V., Orellana-Rodriguez, C., Altingovde, I.S. (2012). Can Social Features Help Learning to Rank YouTube Videos?. In: Wang, X.S., Cruz, I., Delis, A., Huang, G. (eds) Web Information Systems Engineering - WISE 2012. WISE 2012. Lecture Notes in Computer Science, vol 7651. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35063-4_40

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  • DOI: https://doi.org/10.1007/978-3-642-35063-4_40

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-35062-7

  • Online ISBN: 978-3-642-35063-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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